Power harmonics analysis of programmable AC power source via variation semi-sinusoidal window

Harmonic contents are employed for indicating the quality of the programmable AC power source (PACPS). However, spectrum leakage usually occurs. This paper employs a variation semi-sinusoidal window (VSSW) for performing the power harmonic analysis. Since the rolloff of the sidelobes of the VSSW is fast, experimental results show that the VSSW outperforms existing windows.     

Static safety guarantees for a low-level multithreaded language with regions

We present the design of a formal low-level multithreaded language with advanced region-based memory management and thread synchronization primitives, where well-typed programs are memory safe and race free. In our language, regions and locks are combined in a single hierarchy and are subject to uniform ownership constraints imposed by this hierarchical structure: deallocating a region causes its sub-regions to be deallocated. Similarly, when a region is read/write-protected, then its sub-regions inherit the same access rights. We discuss aspects of the integration and implementation of the formal language within Cyclone and evaluate the performance of code produced by the modified Cyclone compiler against highly optimized C programs using pthreads. Our results show that the performance overhead for guaranteed race freedom and memory safety is in most cases acceptable.     

Development of efficient docking strategies and structure-activity relationship study of the c-Met type II inhibitors

c-Met is a transmembrane receptor tyrosine kinase and an important therapeutic target for anticancer drugs. In the present study, we systematically investigated the influence of a range of parameters on the correlation between experimental and calculated binding free energies of type II c-Met inhibitors. We especially focused on evaluating the impact of different force fields, binding energy calculation methods, docking protocols, conformation sampling strategies, and conformations of the binding site captured in several crystallographic structures. Our results suggest that the force fields, the protein flexibility, and the selected conformation of the binding site substantially influence the correlation coefficient, while the sampling strategies and ensemble docking only mildly affect the prediction accuracy. Structure-activity relationship study suggests that the structural determinants to the high binding affinity of the type II inhibitors originate from its overall linear shape, hydrophobicity, and two conserved hydrogen bonds. Results from this study will form the basis for establishing an efficient computational docking approach for c-Met type II inhibitors design.     

三层共挤农膜幅宽自动控制系统设计

为提高大型吹塑设备自动化水平,实现大型幅宽农膜生产制备,设计了三层共挤农膜幅宽自动控制系统;该系统采用主从单片机控制,主要由超声波测距装置和风机控制装置组成;用超声波传感器测出超声探头至泡膜的距离,通过单片机编程算出农膜幅宽,和标准幅宽比较,去控制风机转速;通过调节进风机和排风机的转速,达到控制农膜幅宽的目的;超声波测距采用超声波循环反射测量法,风机调速采用仿人智能控制算法,将控制分为多个模态,以实现10～20m农膜幅宽的实时控制和精确控制;试验结果表明,控制精度较高。     

Robust numerical approach to steady-state calibration of mean-value models

A numerically robust approach to steady-state calibration of nonlinear dynamic models is presented. The approach is based on explicit formulation of the constraints on validity of internal model signals by set of inequalities. The constrained optimization with feasible iterates guarantees that the model will never be evaluated with invalid internal signals. This overcomes numerical difficulties often encountered when dealing with highly nonlinear models. Because the approach uses a large number of slack variables, distributed least squares algorithm is proposed. The robustness of this approach is demonstrated on a steady-state calibration of turbocharged diesel engine model starting from grossly inaccurate initial estimates.     

Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine

Fault diagnosis for wind turbine transmission systems is an important task for reducing their maintenance cost. However, the non-stationary dynamic operating conditions of wind turbines pose a challenge to fault diagnosis for wind turbine transmission systems. In this paper, a novel fault diagnosis method based on manifold learning and Shannon wavelet support vector machine is proposed for wind turbine transmission systems. Firstly, mixed-domain features are extracted to construct a high-dimensional feature set characterizing the properties of non-stationary vibration signals from wind turbine transmission systems. Moreover, an effective manifold learning algorithm with non-linear dimensionality reduction capability, orthogonal neighborhood preserving embedding (ONPE), is applied to compress the high-dimensional feature set into low-dimensional eigenvectors. Finally, the low-dimensional eigenvectors are inputted into a Shannon wavelet support vector machine (SWSVM) to recognize faults. The performance of the proposed method was proved by successful fault diagnosis application in a wind turbine's gearbox. The application results indicated that the proposed method improved the accuracy of fault diagnosis.     

Frequency-domain iterative learning control of a marine vibrator

To the purpose of marine seismic acquisition, new acoustic sources have been developed to reduce the environmental impact. The use of marine vibrators makes it possible to define emission frequency ranges, consequently allowing limitation of the frequencies that disturb marine animal life. Constructing marine vibrators with high efficiency and linear dynamics is however difficult, and the vibrators suffer from both friction, backlash and high-order harmonics. These nonlinear effects, in combination with drifting dynamics, make the required control a crucial and challenging problem. This paper presents a model-based iterative learning control solution, performed in the frequency-domain. Additionally, an adaptive reidentification algorithm is developed to cope with drifting dynamics. The proposed solutions are successfully evaluated in experiments with a marine vibrator.     

Enhanced radio tomographic imaging with heterogeneous Bayesian compressive sensing

This work explores the novel use of Bayesian compressive sensing (BCS) in radio tomographic imaging (RTI), which aims at addressing the performance degradation of shadow fade imaging due to multipath interferences, through the sophisticated efforts on enhancing BCS with the capability of heterogeneous-noise-variance learning. Our contribution is twofold. Firstly, we incorporate a hierarchical model of heterogeneous noise variances into sparse Bayesian learning, which can contribute to the enhancement of BCS in terms of noise-variance awareness. Then, under our enhanced BCS (namely heterogeneous BCS) framework, we develop two learning algorithms for the RTI reconstruction. Theoretical analysis will show the potential advantages of using our heterogeneous BCS in mitigating the effect of multipath interferences, as well as in improving the RTI performance with our learning algorithms. Finally, the experimental results in the context of device-free localization and tracking are reported to demonstrate the effectiveness of the proposed approach.     

Solving parity games by a reduction to SAT

This paper presents a reduction from the problem of solving parity games to the satisfiability problem in propositional logic (SAT). The reduction is done in two stages, first into difference logic, i.e. SAT combined with the theory of integer differences, an instance of the SAT modulo theories (SMT) framework. In the second stage the integer variables and constraints of the difference logic encoding are replaced with a set of Boolean variables and constraints on them, giving rise to a pure SAT encoding of the problem. The reduction uses Jurdziński?s characterisation of winning strategies via progress measures. The reduction is motivated by the success of SAT solvers in symbolic verification, bounded model checking in particular. The paper reports on prototype implementations of the reductions and presents some experimental results.